Enhancing IoT blockchain scalability through the eepos consensus algorithm
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2025.16.2.02Keywords:
Blockchain, Consensus Algorithm, EePoS, Energy Efficiency, IoT, Proof of Stake.Dimensions Badge
Issue
Section
License
Copyright (c) 2025 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The integration of blockchain technology with the Internet of Things (IoT) introduces significant scalability, energy efficiency, and security challenges, particularly when using traditional consensus mechanisms like Proof of Work (PoW). IoT networks generate vast amounts of data while operating under resource constraints, necessitating the development of consensus algorithms that balance energy efficiency, transaction throughput, and security. Addressing these challenges is critical for the sustainable adoption of blockchain in IoT ecosystems. This research aims to enhance blockchain scalability and performance in IoT environments through the development of the Enhanced Efficient Proof of Stake (EePoS) consensus algorithm. The objective is to provide a framework that optimizes validator selection, minimizes energy consumption, and ensures robust security against common blockchain threats. The proposed method employs a multi-layered architecture, selective validation, and a behavior-aware penalty-reward system to ensure efficient consensus. Key security metrics, including Probability of Successful Attack (PSA) and Forking Rate (FR), were evaluated to demonstrate the algorithm’s resilience. EePoS reduces PSA by dynamically adjusting validator selection based on stake, behavior, and transaction load while decreasing FR through cluster-based voting and hierarchical aggregation. Experimental results demonstrated 20% lower PSA, 30% reduced FR, and 8% faster consensus time compared to ePoS. Throughput improved to 296 TPS while reducing CPU and memory utilization, ensuring robust performance for resource-constrained IoT networks. The novelty of this work lies in the tailored enhancements to the PoS framework, specifically designed for IoT constraints, making EePoS a scalable, energy-efficient, and secure solution for IoT blockchain integration.Abstract
How to Cite
Downloads
Similar Articles
- V. Seethala Devi, N. Vanjulavalli, K. Sujith, R. Surendiran, A metaheuristic optimisation algorithm-based optimal feature subset strategy that enhances the machine learning algorithm’s classifier performance , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- S. Deepa, I.S. Arafat, M. Sathya Priya, S. Saravanan, An improved spectrum sharing strategy evaluation over wireless network framework to perform error free communications , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- M. Iniyan, A. Banumathi, The WBANs: Steps towards a comprehensive analysis of wireless body area networks , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- V. Manikandabalaji, R. Sivakumar, V. Maniraj, A novel approach using type-II fuzzy differential evolution is proposed for identifying and diagnosis of diabetes using semantic ontology , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- R. Porselvi, D. Kanchana, Beulah Jackson, L. Vigneash, Dynamic resource management for 6G vehicular networks: CORA-6G offloading and allocation strategies , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Amudavalli L, K. Muthuramalingam, Integrated energy-efficient routing and secure data management for location-aware wireless sensor networks with PFO leveraged improved fuzzy unequal clustering algorithm (IFUC) , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- S Prabhakaran, Yugeshkrishnan M, Santhiya M, Danush Kumar S M, Smart Dustbin using IOT , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- V. Baby Deepa, R. Jeya, Dynamic resource allocation with otpimization techniques for qos in cloud computing , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Purnendu B. Acharjee, Bhupaesh Ghai, Muniyandy Elangovan, S. Bhuvaneshwari, Ravi Rastogi, P. Rajkumar, Exploring AI-driven approaches to drug discovery and development , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- S. Jerinrechal, I. Antonitte Vinoline, A vendor-constrained economic production quantity model integrating scrap recovery under sustainability , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
<< < 6 7 8 9 10 11 12 13 14 15 > >>
You may also start an advanced similarity search for this article.

